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Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outlier intensive and sparse, research has arisen focused on the development of efficient prototype-based clustering methods for more challenging datasets. This collection of articles primarily focuses on developing prototype-based clustering for more scalable, efficient and reliable data processing. To achieve these goals, improvements and modifications have been made to prototype-based clustering in six included articles. Additionally an application of the prototype-based clustering to supervised learning in regression problems is also covered. In general, these efforts advance the knowledge discovery process towards more reliable data processing and big data.